做 AI 文本检测研究的团队终于有了一个能模拟真实渐进编辑过程的基准——它揭示了混合作者文本比纯 AI 文本更难检测的反直觉现象，值得点开看看实验设计。
OpAI-Bench 是一个操作引导的基准，用于研究从纯人类写作到 AI 辅助编辑的渐进式文本转换。它从人类文档出发，在四种领域下构建九个连续修订版本，涵盖五种 AI 编辑操作，并保留多粒度（文档、句子、词元、片段）的作者归属信息。实验发现，AI 文本的可检测性不仅受 AI 编辑比例影响，还与编辑操作、领域和累积修订历史有关，且混合作者的中期版本比纯人类或重度 AI 编辑的端点更难检测。该基准填补了现有检测基准仅关注最终输出的空白，为分析 AI 辅助写作的可检测性提供了受控测试平台。代码和基准已开源。
Operation-Guided Progressive Human-to-AI Text Transformation Benchmark for Multi-Granularity AI-Text Detection
As AI writing assistants become increasingly integrated into real-world drafting and revision workflows, many documents are no longer purely human-written or AI-generated, but instead result from progressive human-AI co-editing. However, existing AI-text detection benchmarks largely focus on final outputs and provide limited understanding of how AI authorship signals emerge, accumulate, or disappear throughout the revision process. We introduce OpAI-Bench, an operation-guided benchmark for studying progressive human-to-AI text transformation across document, sentence, token, and span granularities. Starting from human-written documents, OpAI-Bench constructs nine sequentially revised versions for each sample under predefined AI coverage levels and five representative AI edit operations, covering four domains while preserving complete authorship provenance at multiple granularities. The benchmark supports comprehensive evaluation with 8 document-level detectors, 7 sentence-level detectors, and 2 fine-grained token/span-level detectors. Experiments reveal that AI-text detectability is governed not only by the proportion of AI-edited content, but also by edit operation, domain, and cumulative revision history. Interestingly, we notice that mixed-authorship intermediate versions are often harder to detect than both fully human and heavily AI-edited endpoints, exposing non-monotonic detection patterns missed by existing benchmarks. OpAI-Bench provides a controlled testbed for analyzing whether, when, and how AI-assisted writing becomes detectable under realistic progressive editing scenarios. Our code and benchmark are available at https://github.com/VILA-Lab/OpAI-Bench.